Controlled Gaussian process dynamical models with application to robotic cloth manipulation

Journal Article (2023)


International Journal of Dynamics and Control





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Over the last years, significant advances have been made in robotic manipulation, but still, the handling of non-rigid objects, such as cloth garments, is an open problem. Physical interaction with non-rigid objects is uncertain and complex to model. Thus, extracting useful information from sample data can considerably improve modeling performance. However, the training of such models is a challenging task due to the high-dimensionality of the state representation. In this paper, we propose Controlled Gaussian Process Dynamical Model (CGPDM) for learning high-dimensional, nonlinear dynamics by embedding it in a low-dimensional manifold. A CGPDM is constituted by a low-dimensional latent space with an associated dynamics where external control variables can act and a mapping to the observation space. The parameters of both maps are marginalized out by considering Gaussian Process (GP) priors. Hence, a CGPDM projects a high-dimensional state space into a smaller dimension latent space in which is feasible to learn the system dynamics from training data. The modeling capacity of CGPDM has been tested in both a simulated and a real scenario, where it proved to be capable of generalizing over a wide range of movements and confidently redicting the cloth motions obtained by previously unseen sequences of control actions.


manipulators, nonlinear programming, robot dynamics.

Scientific reference

F. Amadio, J.A. Delgado-Guerrero, A. Colomé and C. Torras. Controlled Gaussian process dynamical models with application to robotic cloth manipulation. International Journal of Dynamics and Control , 11: 3209-3219, 2023.